PUBLICATIONS
Water Resources Research
RESEARCH ARTICLE
10.1002/2013WR014792
Key Points:
Feed for cattle production causes
groundwater stress to crucial U.S.
aquifer systems
Methodology to link groundwater
use and stress to crop types for the
first time
Total uncertainty is largely due to
recharge and irrigation application
efficiency
Supporting Information:
Readme
Supporting text, tables, and figures
Correspondence to:
T. Gleeson,
[email protected]
Citation:
Esnault, L., T. Gleeson, Y. Wada, J.
Heinke, D. Gerten, E. Flanary, M. F. P.
Bierkens, and L. P. H. van Beek (2014),
Linking groundwater use and stress to
specific crops using the groundwater
footprint in the Central Valley and
High Plains aquifer systems, U.S., Water
Resour. Res., 50, 4953–4973,
doi:10.1002/2013WR014792.
Received 25 SEP 2013
Accepted 28 MAY 2014
Accepted article online 2 JUN 2014
Published online 18 JUN 2014
Linking groundwater use and stress to specific crops using the
groundwater footprint in the Central Valley and High Plains
aquifer systems, U.S.
Laurent Esnault1, Tom Gleeson1, Yoshihide Wada2, Jens Heinke3, Dieter Gerten3, Elizabeth Flanary1,
Marc F. P. Bierkens2,4, and Ludovicus P. H. van Beek2
1
Department of Civil Engineering, McGill University, Montreal, Quebec, Canada, 2Department of Physical Geography,
Utrecht University, Utrecht, Netherlands, 3Potsdam Institute for Climate Impact Research, Research Domain of Earth
System Analysis, Potsdam, Germany, 4Unit Soil and Groundwater Systems, Deltares, Utrecht, Netherlands
Abstract A number of aquifers worldwide are being depleted, mainly by agricultural activities, yet
groundwater stress has not been explicitly linked to specific agricultural crops. Using the newly developed
concept of the groundwater footprint (the area required to sustain groundwater use and groundwaterdependent ecosystem services), we develop a methodology to derive crop-specific groundwater footprints.
We illustrate this method by calculating high-resolution groundwater footprint estimates of crops in two
heavily used aquifer systems: the Central Valley and High Plains, U.S. In both aquifer systems, hay and haylage, corn, and cotton have the largest groundwater footprints, which highlights that most of the groundwater stress is induced by crops meant for cattle feed. Our results are coherent with other studies in the
High Plains but suggest lower groundwater stress in the Central Valley, likely due to artificial recharge from
surface water diversions which were not taken into account in previous estimates. Uncertainties of recharge
and irrigation application efficiency contribute the most to the total relative uncertainty of the groundwater
footprint to aquifer area ratios. Our results and methodology will be useful for hydrologists, water resource
managers, and policy makers concerned with which crops are causing the well-documented groundwater
stress in semiarid to arid agricultural regions around the world.
1. Introduction
The global rate of groundwater depletion has more than doubled since the 1960s [Wada et al., 2010] and
even accelerated recently over the 2000–2008 period [Konikow, 2011], which is driven largely by irrigation
demand for agriculture [Aeschbach-Hertig and Gleeson, 2012]. Groundwater abstraction can result in
increased recharge rates sometimes due to irrigation water return flow but more often leads to decreased
groundwater discharge and/or groundwater depletion, defined as the permanent loss of stored groundwater [Aeschbach-Hertig and Gleeson, 2012]. Groundwater depletion occurs when groundwater abstraction
exceeds the capture of groundwater by increased recharge or decreased discharge [Bredehoeft, 2002]. Water
stress is an indicator of water scarcity for humans and is generally defined as the ratio of abstractions to surface water and groundwater availability [Oki and Kanae, 2006]. More recently estimations of water stress or
scarcity have used consumptive use of water as opposed to abstractions, thereby giving a more accurate
portrayal of freshwater depletion [Hoekstra et al., 2012]. Groundwater depletion and stress have been quantified at regional to global scales [Aeschbach-Hertig and Gleeson, 2012; Gleeson et al., 2012; Konikow, 2011;
Wada et al., 2012]. Although groundwater stress is often due to agriculture, it has yet to be explicitly linked
to specific crops.
Blue water is the crucial freshwater resource comprised of surface water such as lakes or rivers and groundwater in aquifers [Falkenmark and Rockstrom, 2006]. Though 85% of global crop water consumption originates from green water resources (the rainwater stored in the soil as soil moisture) [Rost et al., 2008],
agriculture consumes most of the groundwater abstracted, and groundwater accounts for 40% of the total
consumptive irrigation water use globally [Siebert et al., 2010; Wada et al., 2012]. The use of green and blue
water by various crops has been estimated in global hydrologic models such as LPJmL [Gerten et al., 2011]
and H08 [Hanasaki et al., 2010]. Yet never has it been assessed how much of an impact specific crop types
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have on groundwater resources of a specific aquifer system, usually because adequate agricultural and
water use data are not available [Wada et al., 2010].
Our objective is therefore to identify which groundwater-irrigated crops contribute to aquifer stress using
both global and local data sets, and assess the uncertainty of estimates of groundwater stress. To this end,
we have developed a methodology that uses high-resolution agricultural and water use data and we demonstrate how this methodology can be applied to two aquifer systems. Although groundwater depletion is
a global problem [Aeschbach-Hertig and Gleeson, 2012], significant depletion occurs locally in discrete aquifer systems. We therefore focus on two of the most stressed aquifer systems in the U.S. [Gleeson et al.,
2012], where high-resolution agricultural and water use data are readily available: the Central Valley and the
High Plains aquifer systems. The U.S. has indeed the second highest rate of groundwater consumption in
the world [Siebert et al., 2010] and these two aquifer systems have the highest rate of groundwater abstraction in the U.S. [Alley, 2006]. The resulting groundwater depletion poses a threat to the agricultural economy
of the U.S. since market value of agricultural products grown in the Central Valley and the High Plains,
respectively, contributed up to 7% and 11.7% to the nation’s $300 billion in agricultural revenue in 2007
[Scanlon et al., 2012]. To consider the within-aquifer spatial variability, the two aquifer systems were subdivided into their aquifers (e.g., Sacramento Valley, Tulare basin, San Joaquin basin, Eastside, and Delta for the
Central Valley aquifer system) and those aquifers were in turn subdivided into the U.S. counties overlying
them, enabling the use of county-scale reports of both crop areas and groundwater abstractions, which
include a finer spatial variability compared to global data sets. Results have implications for water resource
managers in the area but also extend beyond the U.S. borders through the export of commodities containing ‘‘virtual’’ groundwater.
2. Methodology
2.1. Theory
The groundwater footprint (GF) was first introduced in Gleeson et al. [2012] as the area required to sustain
groundwater use and groundwater-dependent ecosystem services of a region of interest, such as an aquifer, watershed, or community. Since it is effectively an aquifer water balance, we clarify the definition as
‘‘the area required to sustain groundwater use and groundwater-dependent ecosystem services of an aquiC
fer.’’ Its mathematical definition is GF5 R2E
3Aaq , where C, R, and E are, respectively, the area-averaged
annual abstraction of groundwater, recharge rate including artificial recharge (from irrigation), and the
groundwater contribution to environmental streamflow, all in units of L/T such as m/d [Gleeson et al., 2012].
Aaq (units of L2 such as m2) is the areal extent of an aquifer, where C, R, and E are defined. R represents the
sum of long-term natural areal flux into the system and additional artificial recharge from irrigation.
€ll, 2009; Faunt, 2009; van Beek et al.,
Recharge can be derived from global and local hydrologic models [Do
2011] as well as chloride and field data [Scanlon et al., 2012], although it could also be derived from geochemical tracer methods [McMahon et al., 2011]. Sustaining ecosystem services requires that a certain
amount of groundwater (E) be allocated to surface water flow. E is assumed to be a fraction of R for an aquifer [Gleeson et al., 2012] even though environmental flow requirements are best determined by detailed
hydroecological data and multidisciplinary expert consultation [Poff et al., 2009; Richter et al., 2012, 2003;
Smakhtin et al., 2004]. This fraction can be calculated as the ratio of Q90 (the monthly streamflow exceeded
90% of the time, considered as the low flow), to Qavg, the long-term average streamflow [Gleeson et al.,
2012] although Q90 is not always a good representation of groundwaterdischarge.
Aquifer stress is indicated by a ratio of groundwater footprint to aquifer area greater than 1 AGFaq 1 .
The notation section and equations below are as generic as possible so that they can be applied to other
regions of the world, although some variables are defined based on the spatial resolution of data available
in the study area. For example, the co subscript for county is meaningful in the U.S. but could be modified
to a different jurisdictional name or physiographic region with a similar scale in other places. All variables
are defined in the notation section and described in more detail below. We use the term abstraction to designate withdrawal and for clarity, we do not use the term consumption (the difference between abstractions and return flows).
Our methodology is depicted in Figure 1 for the case of corn in the Central Valley aquifer system. The mathematical definition of the groundwater footprint was in this study adapted to use available data.
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Figure 1. Flow chart of data sources and analysis to results for corn in the Central Valley aquifer system.
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Groundwater abstraction as defined above (C) of a given crop type i (such as wheat or corn) is thus alternaGa
tively defined as Ci 5 Ai;aqaq in the groundwater footprint of a given crop type i of an aquifer (equation (1)).
Since recharge, environmental flow requirements and hence groundwater footprint are defined at aquiferscale, the groundwater abstraction of a given crop type i derived from a smaller scale (here, counties) was
amalgamated at aquifer-scale (equation (2)). Crop groundwater abstraction of a given type i was first calculated at county-scale due to the availability of irrigated acreage and irrigation efficiency per crop type i at
this scale (equation (3)). This also required crop type i’s net blue water requirements to be calculated at
county-scale (see section 2.2.2 and Figure 1):
GFi; aq 5
Gai; aq
Raq 2 Eaq
(1)
where the aquifer-scale groundwater abstraction is:
X
Gai; aq 5
Gai; co
(2)
f8 co; co\aq6¼1g
where the county-scale groundwater abstraction is:
Gai; co 5
Bi; co
3Acri; co 3pi
ei; co
(3)
where the total irrigation efficiency is:
ei; co 5ec co 3eai 3em
(4)
As defined in this study, total irrigation efficiency (ei; co ) and irrigated crop area (Acri; co ) do not depend on the
irrigation water type, that is to say groundwater or surface water. Therefore, equation (3) could equally be valid
for surface water by substituting the areal proportion of crop area irrigated by groundwater by its surface water
equivalent. This suggests that with different data this method could be adapted to surface water systems.
The net blue water requirement (Bi ) is the areal flux of blue water needed for a crop to grow optimally,
which implies that deficit irrigation and over irrigation are not considered in this methodology. Such practices could be taken into account by multiplying the net blue water requirement in equation (3) by a term
depending on the crop type and the scale of data available. Furthermore, net blue water requirements do
not capture water lost due to irrigation inefficiencies (ei; co ), which is comprised of three components: conveyance system efficiency (ec co ), application system efficiency (ea i ), and a management factor (em ) (equation
(4)), which are described in supporting information. Dividing Bi by ei; co takes into account return flows (that
B
part of lost water that recharges blue water resources) so that the quantity ei;i; coco 3Acri; co represents an
abstraction.
The variable Acri; co is the irrigated and planted area of a given crop type at county-scale. However, this
quantity may not be readily available in data sets and might need to be represented by irrigated and harvested area instead, which can be less than the planted area (see section 2.2.1). The groundwater-irrigated
and planted area is then derived using the areal proportion of irrigated crop area fed by groundwater (pi ).
Water that is applied for on-field practices (preirrigation, frost protection, application of chemicals, weed
control, leaching salts from the root zone, etc.) can be locally important but is not included here since these
are difficult to estimate and we assume it is likely that they do not represent a significant amount of
groundwater abstraction. Also, gray water (the amount of water that was directly and indirectly polluted to
produce the crops) is not included in our calculations since the groundwater footprint focuses on groundwater quantity and not groundwater quality [Gleeson et al., 2012].
2.2. Data
2.2.1. Data Sources
Table 1 summarizes the hydrologic and agricultural data analyzed. We use long-term averages for recharge,
environmental flow requirement, and net blue water requirement and the most recent data on water use
efficiency and irrigated acreages, generally from the mid or late 2000s. The most recent available data were
used which implies that not all the data have the same temporal resolution or coverage (Table 1). One set
of efficiency data was last collected in the 1990s; we use these data assuming that conditions have not
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Original data
unit
Temporal range
Spatial
resolution
Crop-specific
data
Sources
Term
1958–2000
average
m/yr
N.A.
No
1962–2003 and
1950–2007
km3/yr
0.5 3 0.5
0.5 3 0.5
Yes (Crop
Functional
Type)
1971–2000
average
mm/yr
LPJmL model
[Gerten et al.,
2011]
Bi; co Net Blue
Water
Requirements
PCR-GLOBWB model
[Gleeson et al.,
2012]
Eaq Environmental
Flow Requirements
CVHM model and
measurements
[Faunt, 2009;
Scanlon et al.,
2012]
Aquifer
Raq Recharge
Table 1. Hydrologic and Agricultural Data
2008
dimensionless
acres
Yes (individual
crops)
State
USDA irrigation
survey [NASS,
2008]
2007
Yes (individual
crops)
County
USDA census of
agriculture
[NASS, 2009]
Acri; co Irrigated and
Harvested
Crop Areas
pi Areal Proportion
of Irrigated
Crop Area
Irrigated
by Groundwater
dimensionless
1995
No
County
USGS water use
survey
[Solley et al.,
1998]
ec co Conveyance
System
Efficiency
Gai; co Groundwater Abstraction
dimensionless
2007/2008
Yes (individual crops)
State
USDA irrigation
survey [NASS, 2008]
Method efficiency
[Cooley et al., 2008]
ea i Application
System Efficiency
em Management
Factor
dimensionless
2007
No
Nation
Irrigation efficiency
estimates
[Rohwer et al.,
2007]
ei; co Total Irrigation Efficiency
Mgal/d
2005
No
County
USGS water
use survey
[Kenny et al.,
2009]
Gaco Total
Groundwater
Abstraction
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significantly changed. Long-term yearly averages run over about 30–40 years and do not include data after
2000 exclusive; we consider that those averages are reasonable (assuming a stationary climate) and represent a standard year. Data are derived from three scales: county (average size over U.S. is 3000 km2 with
wide variations form East to West), state (average size over U.S. is 200,000 km2), and nation
(10,000,000 km2). Areas of the High Plains and Central Valley aquifer systems were subdivided into three
and five aquifers, respectively, based on detailed hydrogeological mapping [Faunt, 2009; Weeks et al., 1998].
Irrigation water type is assumed to be taken as blue water from surface or groundwater only. Desalination
and wastewater reclamation (wastewater that has been treated for nonpotable reuse) for irrigation purposes are not considered in this study because they are likely not significant. Saline water sources constitute
a negligible proportion of public supply sources in the U.S. [Kenny et al., 2009] and reclaimed wastewater is
only used on 3.6% and 0.5% of irrigated land in, respectively, California and the states underlain by the
High Plains aquifer system [NASS, 2008]. Recycling (reuse of surface or groundwater that has already been
used to irrigate a crop on the farm) is also a practice that was not considered in this study for the same reasons; 8.8% and 1.3% of irrigated land in, respectively, California and the states underlain by the High Plains
aquifer system use recycled water, but in unknown proportions of irrigated water [NASS, 2008].
The net blue water requirement (Bi ) used in our study is averaged for 12 different crop functional types in
the LPJmL model on a 0.5 -by-0.5 grid (i.e., 50 km-by-50 km grid at the equator) over the period 1971–
2000 [Gerten et al., 2011]. We assigned individual crop types from agricultural census data to each crop functional type (supporting information Table S1). Although horticultural operations (notably tomatoes,
almonds, pistachios, or fruits) may be important in California and Texas [Dickens et al., 2011], they are not
considered here since they are not analyzed as a crop functional type in the LPJmL model. Note that we
used outputs from a global model to simulate Bi instead of crop coefficients used notably by the USGS
[Dickens et al., 2011], because LPJmL dynamically calculates net blue water requirements rates on a daily
basis using soil, precipitation, and temperature data sets [Rost et al., 2008], which is more spatially and temporally accurate (see supporting information for more details on LPJmL).
We used the different types of irrigation efficiencies as identified in Rohwer et al. [2007]. The conveyance
system efficiency (ec co ) was derived from USGS conveyance losses estimates [Solley et al., 1998]. Application
system efficiency (ea i ) was derived from a special tabulation of the USDA Farm and Ranch Irrigation Survey
[NASS, 2008] and Cooley et al. [2008]. The management factor (em ) for the U.S. is given in Rohwer et al.
[2007] (see supporting information for more details).
The total groundwater abstraction for irrigation at county level Gaco is also estimated by the USGS using
various methods depending on states [Kenny et al., 2009], but is only reported as aggregates over all crops.
This data set is compared as explained in section 2.2.2 to our own estimates, when summed over all the
crop types considered. See section 3.1.1 for the results of this comparison and supporting information for
more details on USGS data on groundwater abstraction for irrigation.
Only irrigated and harvested (in contrast to irrigated and planted) areas are reported precisely in the USDA
Census of Agriculture [NASS, 2009]. Though failed crop areas are reported (0.7% of cropland in California
and on average 1.2% of cropland in the states underlain by the High Plains aquifer system), they are aggregated for all crop types that failed and no indication is provided on the stage at which the crops failed,
whereas beyond the crop type itself, such a stage also determines the extent of water used before crop failure. It is also likely that those areas that failed were more of the nonirrigated type since in case of droughts,
for instance, irrigation can be increased on land equipped with irrigation systems to compensate for the
lack of rainfall, which is not the case on rainfed cropland. USDA’s National Agricultural Statistics Service
(NASS) annually releases county-scale estimates on planted crop areas but does not distinguish irrigated
and nonirrigated crop areas within it. The variable Acri; co is therefore here the irrigated and harvested area
of crop type i as reported in the USDA Census of Agriculture, though it would have been preferable to use
the irrigated and planted area had it been available.
The areal proportion of crop area irrigated by groundwater (pi ) was derived at state level and per crop type
from the USDA Farm and Ranch Irrigation Survey [NASS, 2008]. The variable was calculated as the ratio
between the harvested acres of a given crop type irrigated by groundwater from wells (Acrgroundwater ), augmented by 5% of the harvested acres irrigated by water from off-farms suppliers (Acrwater from off farm suppliers ),
and the sum of harvested acres for the same crop type irrigated with groundwater from wells, on-farm
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surface water (Acrsurface water ), and water from off-farm suppliers (equation (5)). The latter is indeed considered to be composed of only 5% of groundwater nationally [USDA, 1997]. We moreover disregarded the
possibility that groundwater may be more resorted to with higher or lower efficiency irrigation systems,
depending on the price of groundwater compared to that of surface water.
p5
Acrgroundwater 10:053Acrwater from off farm suppliers
Acrgroundwater 1Acrsurface water 1Acrwater from off farm suppliers
(5)
Multicropping was implicitly considered as the USDA Census of Agriculture includes acres subject to multicropping in the total irrigated area of each crop sown on the same land at different times. However, we
only implicitly consider multicropping because first, LPJmL does not account for multicropping (net blue
water requirements are for a single cropping cycle) and second, the USDA generally provides no indication
on the period of the year when the crops on multicropped land were grown.
Recharge (Raq ) was derived from different data sets. The global hydrologic model PCR-GLOBWB was used
for both the Central Valley and the High Plains and local data sets were also used from the Central Valley
Hydrological Model (CVHM) [Faunt, 2009; Scanlon et al., 2012] and, for the High Plains, from groundwater
chloride and field data [Scanlon et al., 2012]. Values from PCR-GLOBWB constitute averages over 1958–2000
on a 0.5 -by-0.5 grid [Wada et al., 2010]. Recharge is calculated in the model as the net positive downward
flux from the second to the third soil layers. The CVHM is a detailed three-dimensional MODFLOW-based
model that uses a farm mass balance to estimate evaporation, transpiration, runoff, and deep percolation to
groundwater in the Central Valley [Faunt, 2009]. Environmental flow requirements (Eaq ) are defined as a fraction of recharge from the PCR-GLOBWB model and are calculated as outlined above in theory (see supporting information for a detailed description of the PCR-GLOBWB model).
2.2.2. Data Handling and Comparison
Spatial data were analyzed with ESRI’s ArcMap 10.0 suite. Rates (Raq , Eaq , and Bi; co ) that were calculated on a
0.5 -by-0.5 grid were first downscaled to a 0.01 -by-0.01 grid (1 km-by-1 km grid at the equator; supporting information Figure S1a). This resolution was chosen for the boundaries of the smallest counties in mainland U.S., which are 60 km2, to be reasonably approximated. The 0.01 raster rates were then attributed to
individual aquifers (Raq , Eaq ) or counties (Bi; co ) (supporting information Figure S1b) and weighted. The
weighting factor is equal to the 0.01 raster area divided by the sum of the 0.01 raster areas within the
county or aquifer (supporting information Figure S1c). The weighted 0.01 raster rates for each aquifer or
county were finally summed over the aquifer or county (supporting information Figure S1d). When part of a
county was situated outside of the considered aquifer system boundaries, the values of these rates were
derived for the intersection between the county and the aquifer system areas.
If part of a county was situated outside of an aquifer area, possible modifications to county-scale reported
data such as total groundwater abstraction (Gaco ) and irrigated and harvested crop area (Acri; co ) were
accounted for on a case-by-case basis. As detailed in supporting information, we assigned without modification county-scale reported data to the intersection between county and aquifer areas in the Central Valley,
while we adopted a weighting-factor approach for those county-scale reported data in the High Plains.
The primary focus of this study being the impact of different crop types on groundwater stress, we cannot
use USGS estimates of groundwater abstraction for irrigation directly to calculate crop-specific groundwater
footprints, as they amalgamate all crop types without distinction. However, we can use USGS estimates to
derive the groundwater footprint of agriculture (horticulture excluded; supporting information Table S4)
and compare them to ours through equation (6), which quantifies the total groundwater abstraction for
crop irrigation at county-scale:
Gaco 5
X
i
Bi; co
3Acri; co 3pi
ei; co
(6)
We performed paired statistical tests to assess differences between the two estimates (see supporting information for more details on statistical tests performed). We also compared USGS estimates of both groundwater and surface water abstraction to our estimates of blue water abstraction for irrigation at county-scale
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(equation (6) without the term pi ) in order to observe if a significant difference between the two sets of
groundwater abstraction estimates translates into a significant difference between the two sets of blue
water abstraction estimates in a given area.
2.3. Uncertainty Analysis
The uncertainty of ratios of groundwater footprint to aquifer area was calculated by considering the uncertainty of each parameter in equations (1–4), assuming the uncertainty sources are independent. Since relative uncertainties sum, the relative uncertainty of the ratio of groundwater footprint of a given crop type to
aquifer area is:
GFi; aq
D Aaq
GFi; aq
Aaq
E
5
D Raq
DGai; aq DRaq
aq
1
1
E
Gai; aq
Raq
12 aq
(7)
Raq
where D denotes the absolute uncertainty of the term that follows directly. The relative uncertainty of
groundwater abstraction for irrigation of a given crop type is:
DGai; aq
5
Gai; aq
X
f8 co; co\aq6¼1g
Gai; co 3
h
DBi; co
Bi; co
1
Dei; co
ei; co
1
DAcri; co
Acri; co
Gai; aq
i
1 Dp
pi
i
(8)
DB
We calculated the relative uncertainty of net blue water requirements Bi;i;coco at aquifer-scale since reporting it
at county-scale would not necessarily result in added accuracy in estimating the total uncertainty, as the
other relative uncertainties are reported at significantly larger scales (supporting information Table S2) and
Bi is originally derived at a 0.5 -by-0.5 raster spatial resolution. This simplifies equation (8) as follows:
DGai; aq DBi; co Dei; co DAcri; co Dpi
5
1
1
1
Gai; aq
Bi; co
ei; co
Acri; co
pi
(9)
DB
The uncertainty of net blue water requirements Bi;i;coco is calculated at aquifer-scale following the same analysis performed to obtain net blue water requirement (supporting information Figure S1) and represents the
relative difference between two different outputs of the LPJmL model using two different input precipitation data sets. The first precipitation data set is GPCC v.5 (full reanalysis) which is considered as the reference due to the efforts put into checking the quality of raw station data; the second is CRU TS 3.10 [Harris
et al., 2013; Rudolf et al., 2010].
De
The uncertainty of irrigation efficiency at aquifer-scale ei;i;coco is the sum of relative uncertainties on conveyance loss efficiencies, irrigation application efficiencies, and management factor at aquifer-scale. First, the
uncertainty of conveyance loss efficiency can be high but conveyance losses are only significant for irrigation water from surface water bodies, and the conveyance efficiencies reported in Solley et al. [1998] are
likely overestimated for most counties (generally between 0.9 and 1.0, except for counties in Colorado,
Wyoming and New Mexico—states having few counties underlain by the High Plains aquifer system). We
therefore decided that these values of conveyance efficiencies accurately describe the low conveyance
losses associated with groundwater abstraction for irrigation and consequently assigned a 65% uncertainty
of conveyance efficiency for all states. Second, the uncertainty of irrigation application efficiency was calculated for each crop type at state scale, based on data summarized in supporting information Table S3. Each
state value was assigned to the counties both within that state and the aquifer considered and the aquifer
value was then calculated as the average over all counties within it. Third, we consider the uncertainty of
the management factor to be zero because it is likely implicitly included in the uncertainty of the application techniques (supporting information Table S3), as it is difficult to differentiate the uncertainty that is due
to the technique itself and to the ‘‘management’’ of the equipment.
DAcr
The uncertainty of the harvested and irrigated acreage Acri;i;coco is available at national scale and is assumed to
be the same at aquifer-scale. It is measured as the relative root mean squared error on reported acres of
land in farms and equals 0.25% [NASS, 2009].
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Table 2. Ratios of Groundwater Abstraction to Aquifer Area for Different Data Combinationsa
Sacramento
Eastside
Delta
San Joaquin
Tulare
Northern High Plains
Central High Plains
Southern High Plains
Abstraction: This Paper
Recharge: Scanlon et al. [2012]
and Faunt et al. [2009]
Abstraction: USGS Recharge:
Scanlon et al. [2012] and
Faunt et al. [2009]b
Abstraction: This Paper
Recharge: PCR-GLOBWB
Abstraction:
USGS Recharge:
PCR-GLOBWBa
Gleeson and
Wada [2013]
0.3 6 0.2
1.7 6 1.0
1.3 6 0.8
1.5 6 0.9
0.9 6 0.5
5.2 6 2.3
11.2 6 5.1
15.4 6 8.3
0.6
0.5
0.5
0.8
1.1
2.9
11.2
9.7
2.2 6 1.3
5.2 6 3.1
7.5 6 4.5
13.4 6 8.1
9.9 6 5.5
21.0 6 9.4
12.1 6 5.6
12.2 6 6.6
4.2
6.4
9.3
12.2
13.5
12.1
13.6
8.7
3.3 6 2.5
7.2 6 3.2
11.2 6 11.2
9.0 6 2.0
14.4 6 6.5
4.3 6 1.5
8.2 6 5.7
19.1 6 19.8
a
The first column is considered the most accurate and the corresponding data combination is that retained for discussion on crop-specific groundwater footprints.
The values in the corresponding columns do not display uncertainty ranges because the USGS does not provide any quantitative measure of uncertainty and estimates encompass derivation methods of variable accuracy, depending on the state.
b
The uncertainty of acreages irrigated by any water source (Acrany irrigation water source ) is the same and is measured as the relative standard error of irrigated acres of cropland harvested [NASS, 2008], which is available
at state scale (supporting information Table S2). Using equation (5), the uncertainty of the proportion of irriDAcrany irrigation water source
i
gated acreage being irrigated by groundwater is Dp
pi 523 Acrany irrigation water source and is calculated at aquifer-scale
i
using an average over the number of counties that were assigned the Dp
pi of the state to which they belong.
The uncertainty of recharge is calculated at aquifer-scale following the same analysis performed to obtain
net blue water requirement (supporting information Figure S1) and is estimated by comparing PCRGLOBWB simulated runoff fields to station discharge from the Global Runoff Data Centre (http://www.bafg.
€ll and Fiedler,
de/GRDC) and also by comparing PCR-GLOBWB simulated recharge to that from WaterGAP [Do
2008]. A Monte-Carlo simulation is then performed generating 100 equiprobable realizations of groundwater recharge and the standard deviation is eventually calculated. We refer to Wada et al. [2010] for the
detailed methods. Other recharge data sets (CVHM, chloride, and field data) do not have any uncertainty
provided; we therefore assume they have the same relative uncertainty as that calculated for PCR-GLOBWB.
The uncertainty of environmental flow requirements, measured as a fraction of recharge, was not included
since we consider environmental flow requirements as being primarily a management decision at state to
nation scales.
3. Results and Discussion
We first compare the different results obtained with various combinations of data sets to illustrate the
impact of input data on results. Then we examine the uncertainty of the results and the contribution of all
input data to this uncertainty. Finally, we assess the results and their implications before discussing the
applicability of the methodology to other areas.
3.1. Impact of Different Data Sets
As groundwater footprint to aquifer area ratios depend on groundwater abstraction and recharge, we compared two different data sets for each of them, as listed in Table 2.
3.1.1. Abstraction Data Sets
In the High Plains, our estimates of both groundwater and blue water abstraction are higher but not significantly different from the USGS’ at a 95% confidence. For groundwater abstraction, the abstractionweighted relative difference to USGS estimates over all counties is 250% and for blue water abstraction,
258%. In the Central Valley, the difference between USGS groundwater abstraction estimates and ours is
not significant either (abstraction-weighted relative difference to USGS estimates of 23%) though in the
case of blue water abstraction estimates, USGS’ are significantly higher (abstraction-weighted relative difference to USGS estimates of 37%).
Differences between the two data sets arise from many different factors. For instance, we use detailed highresolution reported data in conjunction with modeled data from larger scales, which we downscaled to
county level. Some crop functional types do not suit well certain specific crop types, either because some
individual crops had to be assigned the closest net blue water requirement we could find (for instance,
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cotton has the net blue water
requirement of the pasture crop
functional type) or because some
crop types are inherently difficult
to model due to numerous varieties and ways of cultivation
(supporting information Table
S1). Note that wheat, maize (i.e.,
corn), and rapeseed are the best
validated crop functional types in
LPJmL. To reduce the sources of
errors, the global models used in
this study could be run with
higher resolution input data and
other global models such as H08
[Hanasaki et al., 2010] could also
be used (cotton is, for instance,
explicitly modeled in the latter).
The USGS estimation methods
are poorly documented and vary
greatly from state to state, thus
not providing consistent accuracy between states [Dickens
et al., 2011].
Figure 2. Contribution of parameter uncertainty to total relative uncertainty for (a) the
Central Valley and (b) High Plains aquifer systems. The first value indicates the actual
value of the relative uncertainty of the parameter in the aquifer system and the second
value (%, in bold) indicates the relative contribution of this uncertainty to the total relative uncertainty in the aquifer system.
The temporal resolution between
the two sets of estimates is also
not the same. This bears importance when considering that
there are wide interannual variations in terms of droughts, notably in California, where severe
droughts occurred between 2007
and 2009 [Famiglietti et al., 2011;
Jones and Nguyen, 2010]. It may
therefore be likely that the harvested and irrigated acreage data
retrieved in 2007 were lower that
year compared to what it was in
the years preceding the 2005
USGS estimation of groundwater
abstraction, as shown by the comparison between the 2007 and
2002 acreage data [NASS, 2009].
Since each state has its own
methodology to estimate
groundwater and blue water
abstractions, we also compare
our estimates to USGS’ for each of those three states with a sufficient number of pair differences to derive
statistically robust conclusions (Kansas, Nebraska, and Texas). The USGS estimates were not found to be significantly different from ours, for groundwater abstraction in Texas (224%) and Nebraska (3%, one outlier
county removed) and for blue water abstraction in Nebraska (6%, one outlier county removed) and Kansas
(238%). USGS abstraction estimates of groundwater are only significantly lower than ours in Kansas
(238%) and USGS abstraction estimates of blue water are only significantly higher than ours in Texas (30%).
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In their documentation of methods of USGS irrigation data, Dickens et al. [2011] compare USGS blue water
abstraction estimates to estimates from the Indirect Irrigation Withdrawal Estimation Method (IIWEM) in a
few states. IIWEM blue water abstraction estimates were significantly higher in California (16%) and in Texas
(124%) while they were not significantly different in New Mexico and Wyoming. The good statistical correlation suggests our estimates of groundwater abstraction per crop type are reasonable; we therefore use
them to derive results discussed in the subsequent analysis.
3.1.2. Recharge Data Sets
Groundwater recharge is generally more difficult to obtain than groundwater abstraction and is subject to
high uncertainty [Gleeson and Wada, 2013]. We compared in the High Plains PCR-GLOBWB estimates to estimates based on chloride and field data [Scanlon et al., 2012]. The PCR-GLOBWB recharge rate in the Northern High Plains (9.5 6 1.6 mm/yr) is significantly lower than the corresponding long-term annual and spatial
mean recharge rate as reported in Scanlon et al. [2012] (39.0 mm/yr, no uncertainty range). PCR-GLOBWB is
known to have low recharge in the Northern High Plains, a phenomenon that is not captured in the parameter uncertainty analysis since the deviation from 9.5 mm/yr is only 1.6 mm/yr, leading to ratios being overestimated in the Northern High Plains with the PCR-GLOBWB recharge value. Nevertheless, in both the
Central and Southern High Plains, our recharge estimates are consistent with Scanlon et al. [2012]: PCRGLOBWB models 8.3 6 1.3 and 11.7 6 2.0 mm/yr in, respectively, the Central and Southern High Plains while
Scanlon et al. [2012] finds 10.0 and 10.3 mm/yr, respectively, thus suggesting the model estimates in this
region are accurate. Different geochemical tracer methods summarized in McMahon et al. [2011] give
between 20 and 1200 mm/yr in the Northern High Plains and 7 mm/yr in the Central High Plains.
The comparison of PCR-GLOBWB recharge estimates to CVHM [Faunt, 2009] in the Central Valley highlights
significant discrepancies. The CVHM finds between 8 and 15 times higher recharge than PCR-GLOBWB
depending on the aquifer (Sacramento: 344 mm/yr versus 45.8 6 7.6 mm/yr or San Joaquin: 241 mm/yr versus
16.1 6 2.7 mm/yr). Both models include recharge from precipitation and irrigation. But while the CVHM is a
local highly parameterized model, PCR-GLOBWB is a global 0.5 grid-resolution model. Recharge from largescale surface water diversions like the Central Valley Project or California State Water Project is therefore only
taken into account in the local model. As a means of comparison, different geochemical tracer methods yield
modern recharge rates between 420 and 580 mm/yr over the entire Central Valley [McMahon et al., 2011].
3.2. Total Groundwater Stress and Uncertainty
The uncertainty of groundwater footprint to aquifer area ratios, we derive is obtained by propagating the
uncertainty of different parameters in equation (3). This method allows assessing which of the parameters
account for the highest relative uncertainty (Figure 2), although this also depends on how that uncertainty
was calculated. For instance, this uncertainty analysis does not capture the uncertainty due to the assignment of USDA-listed crop types to crop functional types, which may result in actual uncertainty of net blue
water requirement being higher for certain crop types, notably cotton (supporting information Table S1).
Note that consistent with previous studies, we made best estimates and uncertainty precise up to one decimal place though it sometimes leads to uncertainty having two significant digits (Table 2). Finally, the
uncertainty of the groundwater footprint can be visualized by comparing the areal extent of the best estimate with that of the lower and upper limits of the footprint of all aquifers in both the Central Valley (Figure
3a) and the High Plains (Figure 3b).
3.2.1. Contribution of Parameter Uncertainty to Total Uncertainty
Figure 2 was drawn by first averaging crop-specific quantities over all crops in each aquifer system and
then by taking the area-weighted average of each parameter uncertainty over all aquifers in each aquifer
system. This enables having one diagram for each aquifer system, which is justified by aquifer systems having similar parameter uncertainty values in a given aquifer system.
The contribution of each parameter uncertainty to total relative uncertainty is similar between the Central
Valley and the High Plains (Figure 2) although slight differences can be noted. Both recharge and irrigation
application efficiency yield the highest contribution to total relative uncertainty in both aquifer systems;
however, the biggest uncertainty contributor is irrigation application efficiency in the Central Valley (34.5%)
while it is recharge in the High Plains (34.1%). While the actual value of the relative uncertainty of recharge
is very close in both the Central Valley and the High Plains (0.163 and 0.165, respectively), this difference in
first contributors is due to the actual value of irrigation application efficiency being higher in the Central
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Figure 3. Visualization of how uncertainty impacts the groundwater footprint for (a) the Central Valley and (b) the High Plains aquifer systems. All maps shown at the same scale with the groundwater footprint in gray, 21r lower limit in white and 11r limit in black.
Valley (0.188) than in the High Plains (0.135). This originates from California having a significantly lower irrigation application efficiency for important crops (notably corn, hay, and pasture) than most states overlapping the High Plains (supporting information Table S5); though normally the value of a best estimate is not
indicative of its uncertainty, the most inefficient irrigation methods (surface ditch/furrow, wild flooding,
etc.) are often associated with bigger uncertainty ranges (supporting information Table S3).
The contribution of uncertainty of net blue water requirements is higher in the Central Valley (17.1%) than
in the High Plains (12.1%), as is the actual value of the relative uncertainty of net blue water requirements
(0.093 in the Central Valley versus 0.058 in the High Plains). This is explained by a bigger discrepancy
between the two precipitation data sets used to derive the uncertainty of net blue water requirement in
the Central Valley than in the High Plains, most likely because California experiences more extreme spatial
[Scanlon et al., 2012] and temporal [NOAA, 2014] patterns of precipitation than in the High Plains.
The inverse applies for the proportion of irrigated acreage irrigated by groundwater, whose relative uncertainty contributes this time more in the High Plains (15.1%) than in the Central Valley (8.8%) to the total relative uncertainty. The actual value of the relative uncertainty of the irrigated acreage irrigated by
groundwater is also higher in the High Plains (0.073) than in the Central Valley (0.048).
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3.2.2. Patterns of Total Groundwater Stress
Figures 4p and 5t present the ratios of the total groundwater footprint to aquifer area for the Central Valley
and High Plains aquifer systems, respectively, and use the combination of abstraction data from this study
and recharge data from Scanlon et al. [2012]. The northern most aquifer in the Central Valley, underlying
the Sacramento Valley, has a lower ratio of total groundwater footprint to aquifer area (0.3 6 0.2) than the
remainder of the aquifers (Figure 4p); overlapping uncertainty ranges of ratios in the Eastside, Delta, San
Joaquin, and Tulare aquifers make it impossible to further distinguish a finer stress pattern in the Central
Valley (Table 2). The pattern identified in Figure 4p agrees though with the fact that 80% of groundwater
depletion in the river basin containing the Central Valley aquifer system occurred in the south part, containing the Delta, Eastside, San Joaquin, and Tulare aquifers [Famiglietti et al., 2011]. Note that the Delta and
Eastside aquifers have the size of one to two 0.5 rasters (Figure 1), which makes their ratios more susceptible to spatial errors or bias in the models used in this study to derive environmental flow requirement (PCRGLOBWB) and net blue water requirement (LPJmL), indicating the data resolution may be too coarse for
these areas. This source of error is not taken into account in the uncertainty analysis carried out in this
study.
In the Central Valley, the large discrepancy in ratios between different data combinations and previous estimates (Table 2) is mostly due to the difference in recharge data sets discussed earlier. Predevelopment
recharge in the Central Valley (48.2 mm/yr, before 1962) [Faunt, 2009; Scanlon et al., 2012], i.e., before the
onset of irrigation and the advent of large-scale surface water diversions in California, is close to PCRGLOBWB’s recharge estimates (from 16.1 mm/yr in the San Joaquin aquifer to 45.8 mm/yr in the Sacramento aquifer), suggesting that surface water diversions could provide most of the recharge. Note however
the return flow is generally of lesser quality than original groundwater [McMahon et al., 2008]. Besides, this
may displace resource overexploitation from unsustainable groundwater pumpage (i.e., without artificial
recharge from surface water diversions) to surface water capture endangering sensitive species [Yoshiyama
et al., 1998, 2000]. The possibility of surface water not being diverted in 2014 due to extreme droughts may
also limit the amount of aquifer recharge but also increase groundwater abstraction, a feedback that might
possibly increase groundwater stress in the Central Valley.
Deriving a clear stress pattern in the High Plains is made difficult due to overlapping uncertainty ranges of
the ratios of all three aquifers there. However, the Northern High Plains aquifer may have a lower ratio than
the more southern ones (Table 2), which would match depletion gradients and cumulative change in
groundwater storage identified by Scanlon et al. [2012] in the High Plains.
While the High Plains aquifer system is stressed, the Central Valley might not be stressed given the uncertainty calculated (supporting information Table S2). This highlights a crucial difference between the notions
of stress and depletion: while the Central Valley is experiencing significant groundwater depletion, this is a
long-term trend that does not necessarily result in the aquifer system being stressed continuously. Scanlon
et al. [2012] indeed report that the cumulative change in groundwater storage in the Central Valley is nonmonotonic (stress due to droughts alternating with partial replenishment) despite an overall decline trend
(depletion) driven by the Tulare aquifer. As already pointed out, our study uses climate-dependent parameters that are averages over long temporal ranges (Table 1) and the use of recent acreage data is intended to
show stress conditions in a given standard recent or near future year.
Discrepancies in ratios between previous estimates and ours may to a lesser extent be due to our study taking into account groundwater abstraction for crop irrigation—the focus of this study—while Gleeson and
Wada [2013] also consider other water use categories (public supply, industrial, irrigation, mining, etc.). In
California, 78% of groundwater withdrawn is used for irrigation of crops and the proportion is 73% on average in the states underlain by the High Plains. Besides, estimates from Gleeson and Wada [2013] presented
in Table 2 are calculated from two different abstraction data sets (notably USGS data) and four recharge
data sets (of which only PCR-GLOBWB data estimate artificial recharge from irrigation however).
Horticultural operations are not included in our estimates of groundwater abstraction for irrigation though
they may contribute significantly to groundwater demand in California (particularly tomatoes, almonds, pistachios, or fruits) and Texas [Dickens et al., 2011] notably because some horticultural species have high net
water requirements. In such a likely case, horticulture would stress even more the whole Central Valley aquifer system and the Southern High Plains aquifer.
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Figure 4. Ratio of groundwater footprint to aquifer area for crop types in the Central Valley aquifer system.
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Figure 5. Ratio of groundwater footprint to aquifer area for crop types in the High Plains aquifer system.
We did not account for seasonal groundwater use variations because the input data sets we used do not
provide indications on cropping seasons as well as their associated acreage and associated net blue water
requirement for each crop type. Nevertheless, irrigation exhibits a large seasonal water demand depending
on the cropping seasons and seasonal variations of crop-specific water demand, but also groundwater
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abstraction, recharge, and environmental flow requirements, which could result in highly useful groundwater footprint temporal patterns.
3.3. Aquifer-Scale Groundwater Stress for Individual Crops
Figures 4 and 5 also present the ratios of groundwater footprint to aquifer area for crop types in the Central Valley and High Plains aquifer systems. Note that some crop types do not grow (or have negligible
acreage) over one of the two aquifer systems. For example, rice is grown in the Central Valley but not in
the High Plains whereas soybean is grown in the High Plains and not in the Central Valley. An additional
difference is that corn is divided in the High Plains into corn for grain, corn for silage and popcorn, which
allows us to assess the differences between types of corn in this region of high density of corn-fed livestock farming.
Groundwater footprint to aquifer area ratios exhibits significant spatial variations, both for a same crop
type between the two aquifer systems, and between different crop types in a given aquifer system (Figures 4 and 5). When comparing crops within a given aquifer, differences in ratios are due to net blue
water requirement, acreage, irrigation application efficiency, and proportion of irrigated acreage irrigated by groundwater. Note that recharge estimates are not dependent on crop in a given aquifer.
Even though total irrigation efficiency is crop dependent, it has in fact limited influence on ratios in this
study since the total irrigation efficiency does not vary considerably between crop types (supporting
information Table S5).
In the Central Valley, the highest values of the ratio in the northern most Sacramento Valley are due to hay and
haylage, rice as well as pasture (Figures 4e, 4h, and 4o) though no individual crop stresses the aquifer
GFi;aq
Aaq < 1 . In the central part of the aquifer system (San Joaquin, Eastside, and Delta), hay and haylage, corn,
and pasture account for high values of the ratio (Figures 4e, 4a, and 4o); only do hay and haylage possibly
stress those central aquifers however (Eastside: 0.7 6 0.5; Delta: 0.6 6 0.4; San Joaquin: 0.6 6 0.4). Cotton is also
prominent in the San Joaquin and Tulare aquifers (Figure 4c) but leads to no individual stress. Besides, hay and
haylage, and corn have the most important but nonstressing ratios in the Tulare aquifer (Figures 4e and 4a).
The Northern High Plains aquifer is stressed by corn for grain (Figure 5a), and likely by hay and haylage (Figure 5g, ‘‘likely’’ indicating the lower bound of the uncertainty range does not lead to stress unlike the
upper), while soybean and pasture are also amongst higher ratios, though nonstressing (Figures 5s and 5p).
The Central High Plains is stressed by corn for grain and wheat (Figures 5a and 5m), and likely by hay and
haylage (Figure 5g). Finally, cotton has a stressing ratios in the Southern High Plains aquifer (Figure 5e), and
likely hay and haylage, as well as corn for grain (Figures 5g and 5a).
Hay and haylage (containing mostly alfalfa) significantly contribute to high groundwater footprint to aquifer
area ratios in all aquifers in the Central Valley (Figures 6a–6e) and High Plains (Figures 6f–6h). This is due to
a very high acreage in some counties and a high net blue water requirement. As the net blue water requirement was assigned to be that of the pasture crop functional type (supporting information Table S1), the
ratio values for hay and haylage should be considered carefully however. Corn also has a relatively high
groundwater footprint to aquifer area ratio in most aquifers, notably in the Northern and Southern High
Plains (Figures 6f and 6g) and Delta, Eastside, and San Joaquin (Figures 6b–6d). In this case, it is more due
to high acreage over most counties compared to hay and haylage since the net blue water requirement of
corn is on average about 60–85% that of pasture, depending on aquifer system. The proportion of irrigated
corn-for-grain acreage irrigated by groundwater is high in the High Plains, ranging from 73 to 97% in the
states overlying the High Plains aquifer system (supporting information Table S6), except for Wyoming
(44%), which only intersects a small portion of it though. Moreover, corn-induced groundwater stress may
have been exacerbated by political and economic decisions. The U.S. government indeed introduced highsubsidy programs for corn, notably through the 2005 Energy Policy Act which called for increasing cornderived ethanol production, which we believe might have led to an average 32% increase in corn acreage
between 2002 and 2007 in all the states underlain by the High Plains aquifer system [NASS, 2009].
The crops that most often have the highest ratios (pasture, corn for grain, and hay and haylage) are those
crops that cattle are mostly fed on. This is consistent with beef having the highest water footprint of all animal and crop products [Hoekstra, 2012]. On another hand, many crops for direct human consumption do
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Figure 6. Contribution of crop types to total groundwater footprint to area ratio. Note that the colors are different between aquifers in the Central Valley and High Plains.
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not individually yield a stressing ratio in either aquifer systems, for instance popcorn, beans, sugarbeets,
sunflower, canola, or peanut (Figures 6a–6h).
Cotton has a high ratio in the Southern High Plains (9.2 6 5.1; Figure 5e) as it is a prominent crop in Texas
(Figure 6h), which accounts for 1/4 of total cotton production in the U.S. [TSHA, 2012] and where 88% of
the irrigated cotton acreage is irrigated with groundwater (supporting information Table S6). Like hay and
haylage, cotton was assigned the high net blue water requirement of the pasture crop functional type; its
ratio should therefore also be considered carefully. Moreover, Texas has the largest share of cotton production in the U.S. (30% in 2006) while 68% of U.S. cotton is exported to major textile-producing countries,
mainly China, Turkey, and Mexico [Robinson et al., 2006]. This suggests cotton exports contribute heavily to
local aquifer stress, by being responsible for a groundwater footprint to Southern High Plains aquifer area
ratio of 6.2. In California, 95% of cotton grown is exported [WFPEI, 2012], likely representing a groundwater footprint to aquifer area ratio of 0.2 to 0.3 in the Tulare and San Joaquin aquifers, imputable to cotton
exports only. This is consistent with the U.S. being the third biggest country, in terms of volume, exporting
cotton-virtual-water worldwide [Hoekstra and Chapagain, 2008]. Note that some of the U.S. cotton exported
for processing will return to the U.S. as textile.
Rice acreage in the northern Central Valley is high (Figure 6a and supporting information Figure S2), almost
the same acreage as corn for the entire Central Valley aquifer system. Nevertheless, rice only yields a low
ratio there (0.08 6 0.04; Figure 4h) despite being one of the highest in this area, which is due to only 18% of
irrigated rice acreage using groundwater in California (supporting information Table S6). California moreover accounts for 20% of the U.S. rice production [Sumner and Brunke, 2003]. Exports of Sacramento
Valley-grown rice thus represent a groundwater footprint to aquifer area ratio of 0.04. Californian rice is in
majority high-quality medium japonica rice [Sumner and Brunke, 2003] that is used in sushi production. In
2001 and 2002, a mean of 46% of California-grown rice was exported, about half of which to Japan, 15%
to Turkey and Jordan and 13% to Taiwan [Sumner and Brunke, 2003].
Note that total irrigation efficiency does not change significantly between crop types, ranging from 0.62
(rice) to 0.69 (barley) in the Central Valley and from 0.69 (rice and pasture) to 0.73 (corn for grain, wheat,
and soybean) in the High Plains.
3.4. Applicability of the Method to Other Areas
This study was carried out for the Central Valley and High Plains aquifer systems partly because there are
high-resolution agricultural and water use data readily available in the U.S. However, other areas of the
world are likely to be much data-poorer. Our methodology and equations can easily be adapted to available
data, which will determine the spatial resolution and accuracy of the estimated ratios of groundwater footprint to aquifer area.
Data in our methodology include irrigated acreage, proportion of irrigated acreage irrigated by groundwater,
total irrigation efficiency, net blue water requirement, recharge, and environmental flow requirements (equations (1–3)). As LPJmL and PCR-GLOBWB are global models, access to the three latter data is ensured. However, the most accurate ratios should be obtained if recharge data taking into account local complexities are
available; it is likely they are not crop-specific nevertheless. The need for large-scale hydrological models
such as PCR-GLOBWB arises in places where local precise recharge data are not available and/or where the
irrigation system is not overly complex, as evidenced by the good fit between modeled estimates and measurements in the central and southern parts of the High Plains. Gross efficiency values are also readily available for different regions of the world [Rohwer et al., 2007] though they are available per nation and not
crop-specific. Irrigated acreage can be derived from global data sets such as the one detailed in Ramankutty
et al. [2008] coupled with Portmann et al.’s [2010], as previously used in LPJmL [Gerten et al., 2011].
The proportion of irrigated acreage irrigated by groundwater can be derived from multiple sources [de
€ll et al., 2012]. Totals can be checked if locally derived groundwater data are available; if
Graaf et al., 2014; Do
not, lower-spatial resolution groundwater abstraction data can also be used, such as the IGRAC database
(http://www.un-igrac.org/) at nation-scale, which can be downscaled to obtain groundwater abstraction at
finer resolutions [Wada et al., 2010]. In this case however, one would need to have access to data on how
much of this groundwater abstraction value is devoted to crop irrigation and to which aquifer or aquifer
system.
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4. Conclusions
We calculated for the first time the groundwater footprint of 19 different crops (or crops amalgamations)
and pasture at aquifer-scale in the two most used aquifer systems of the U.S., the Central Valley and High
Plains. We used high-resolution agricultural data from U.S. government organizations along with simulated
net blue water requirement, groundwater recharge, and environmental flow requirements from global
models that we analyzed to obtain a consistent scale to calculate groundwater footprint estimates.
Recharge estimates were also obtained from chloride and field data, and from a local groundwater model.
1. Hay and haylage and corn for grain are the crops that stress groundwater in both aquifer systems the
most; the two account, respectively, for 18% and 56% of the total groundwater footprint in the High Plains,
and for 38% and 19% of the total groundwater footprint in the Central Valley, highlighting the predominant
role of crops grown for cattle feed in the depletion of the Central Valley and High Plains aquifer systems.
Cotton, which is mostly exported, also account for a high ratio notably in the Southern High Plains.
2. Our estimates of groundwater footprint to aquifer area ratios are coherent with other studies in the High
Plains though they are lower in the Central Valley compared to previous estimates of groundwater footprints to aquifer area ratio [Gleeson and Wada, 2013; Gleeson et al., 2012; Scanlon et al., 2012]. We believe
those differences are attributable to surface water diversions for irrigation in California, which were not
taken into account in previous estimates. We therefore consider our estimates to constitute a notable
improvement, all the more since they encompass higher resolution agricultural data.
3. Uncertainties of recharge and irrigation application efficiency contribute the most to the total relative
uncertainty of the groundwater footprint to aquifer area ratios. These two variables constitute 30% of the
total relative uncertainty in both the Central Valley and High Plains aquifer systems. Other variables that
contribute significantly to the total relative uncertainty include net blue water requirements, proportion of
irrigated acreage irrigated with groundwater, and conveyance efficiency.
As described above, our methodology and equations can easily be adapted to other areas with different
available data. The crop-specific methodology presented in this study allows a better evaluation of the
impacts of economic policies on groundwater depletion, and in a broader way on the environment. In addition, the methodology can also show the consequences of consumer choices, such as eating red meat, on
groundwater stress and can be used to educate the general public on the impact of their consumption
choices. Subsequent improvements in the models we used would help further refine our estimates; for
instance, considering more crop functional types and calibrate them to better validate results in the global
hydrological model LPJmL or quantifying recharge and environmental flow requirements better and validating them against measurements. Groundwater and environmental flows are now being implemented in
LPJmL [Pastor et al., 2013], which eventually will allow for internally consistent assessments of these processes without the need to combine different models. In an enhanced model version, recharge could also be
crop- and irrigation method-specific since for instance paddy fields use mostly flood irrigation, which is
likely to induce higher groundwater recharge than other irrigation methods. Running global or regional
models (such as SWAT models) with input data available on a finer spatial resolution may also help in making estimates more accurate as a spatial resolution of 0.5 can be rather coarse for regional applications
such as the one performed here. Furthermore, our estimates could be compared with the 2010 USGS Estimated Water Use in the U.S., when available, which should include a better quantification of what irrigation
water use includes, or at least for a quantification of state estimates accuracy, notably since the guidelines
for the next report specifically refer to this [Kenny et al., 2009]. Our methodology could be useful for hydrologists, water resource managers, and policy makers concerned with which crops are causing the welldocumented groundwater stress in semiarid to arid agricultural regions around the world.
Notation
i
co
aq
GFi; aq
ESNAULT ET AL.
subscript indicating a given crop type (e.g., wheat).
subscript indicating data reported or downscaled to county-scale (more accurately co designates the scale of the intersection area between county and aquifer).
subscript indicating data aggregated over aquifer-scale.
groundwater footprint, m2.
C 2014. American Geophysical Union. All Rights Reserved.
V
4971
Water Resources Research
aquifer area, m2.
recharge, m/yr.
environmental flow requirements, m/yr.
groundwater abstraction for irrigation, m3/yr.
total groundwater abstraction for irrigation, m3/yr.
net blue water requirement, m/yr.
irrigated crop area, m2.
areal proportion of irrigated crop area irrigated by groundwater, dimensionless.
total irrigation efficiency, dimensionless.
conveyance system efficiency, dimensionless.
application system efficiency, dimensionless.
management factor, dimensionless.
Aaq
Raq
Eaq
Gai; co ; Gai; aq
Gaco
Bi; co
Acri; co
pi
ei; co
ec co
ea i
em
Acknowledgments
We appreciate the input from Steve
Sakri (NASS Crops Branch) on Quick
Stats and from Bridget Scanlon (Texas
Bureau of Economic Geology,
University of Texas at Austin) who read
an early version of this manuscript.
The manuscript was also significantly
improved by the suggestions of three
anonymous reviewers, the Associate
Editor and the Editor. The data are
freely available by contacting the
corresponding author.
ESNAULT ET AL.
10.1002/2013WR014792
References
Aeschbach-Hertig, W., and T. Gleeson (2012), Regional strategies for the accelerating global problem of groundwater depletion, Nat. Geosci., 5(12), 853–861.
Alley, W. M. (2006), Tracking U.S. groundwater: Reserves for the future?, Environment, 48(3), 10–25.
Bredehoeft, J. D. (2002), The water budget myth revisited: Why hydrogeologists model, Ground Water, 40(4), 340–345.
Cooley, H., J. Christian-Smith, and P. H. Gleick (2008), Agricultural Water Conservation and Efficiency in California, Special Focus on the Delta,
Pac. Inst., Oakland, Calif.
de Graaf, I. E. M., L. P. H. van Beek, Y. Wada, and M. F. P. Bierkens (2014), Dynamic attribution of global water demand to surface water and
groundwater resources: Effects of abstractions and return flows on river discharges, Adv. Water Resour., 64, 21–33.
Dickens, J. M., B. T. Forbes, D. S. Cobean, and S. Tadayon (2011), Documentation of methods and inventory of irrigation data collected for
the 2000 and 2005 U.S. Geological Survey Estimated use of water in the U.S., comparison of USGS-compiled irrigation data to other
sources, and recommendations for future compilations, U.S. Geol. Surv. Sci. Invest. Rep., 2011–5166, 60 p.
D€
oll, P. (2009), Vulnerability to the impact of climate change on renewable groundwater resources: A global-scale assessment, Environ. Res.
Lett., 4, 035006.
D€
oll, P., and K. Fiedler (2008), Global-scale modeling of groundwater recharge, Hydrol. Earth Syst. Sci., 12(3), 863–885.
D€
oll, P., H. Hoffmann-Dobrev, F. T. Portmann, S. Siebert, A. Eicker, M. Rodell, G. Strassberg, and B. R. Scanlon (2012), Impact of water withdrawals from groundwater and surface water on continental water storage variations, J. Geodyn., 59–60, 143–156.
Falkenmark, M., and J. Rockstrom (2006), The new blue and green water paradigm: Breaking new ground for water resources planning and
management, J. Water Resour. Plann. Manage., 132(3), 129–132.
Famiglietti, J. S., M. Lo, S. L. Ho, J. Bethune, K. J. Anderson, T. H. Syed, S. C. Swenson, C. R. de Linage, and M. Rodell (2011), Satellites measure
recent rates of groundwater depletion in California’s Central Valley, Geophys. Res. Lett., 38, L03403, doi:10.1029/2010GL046442.
Faunt, C. C. (2009), Groundwater availability of the central valley aquifer, California, U.S. Geol. Surv. Prof. Pap., 1766, 227 p.
Gerten, D., J. Heinke, H. Hoff, H. Biemans, M. Fader, and K. Waha (2011), Global water availability and requirements for future food production, J. Hydrometeorol., 12(5), 885–899.
Gleeson, T., and Y. Wada (2013), Assessing regional groundwater stress for nations using multiple data sources with the groundwater footprint, Environ. Res. Lett., 8(4), 044010.
Gleeson, T., Y. Wada, M. F. P. Bierkens, and L. P. H. van Beek (2012), Water balance of global aquifers revealed by groundwater footprint,
Nature, 488(7410), 197–200.
Hanasaki, N., T. Inuzuka, S. Kanae, and T. Oki (2010), An estimation of global virtual water flow and sources of water withdrawal for major
crops and livestock products using a global hydrological model, J. Hydrol., 384(3–4), 232–244.
Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister (2013), Updated high-resolution grids of monthly climatic observations—The CRU TS3.10
Dataset, Int. J. Climatol., 34, 623–642.
Hoekstra, A. Y. (2012), The hidden water resource use behind meat and dairy, Animal Frontiers, 2(2), 3–8.
Hoekstra, A. Y., and A. K. Chapagain (2008), Globalization of Water: Sharing the Planet’s Freshwater Resources, Blackwell, Oxford, U. K.
Hoekstra, A. Y., M. M. Mekonnen, A. K. Chapagain, R. E. Mathews, and B. D. Richter (2012), Global monthly water scarcity: Blue water footprints versus blue water availability, PLoS ONE, 7(2), e32688.
Jones, J., and T. Nguyen (2010), California’s drought of 2007–2009, report, 116 pp., Calif. Dep. of Water Resour. Calif., Sacremento.
Kenny, J. F., N. L. Barber, S. S. Hutson, K. S. Linsey, J. K. Lovelace, and M. A. Maupin (2009), Estimated use of water in the U.S. in 2005, U.S.
Geol. Surv. Circ., 1344, 52 pp.
Konikow, L. F. (2011), Contribution of global groundwater depletion since 1900 to sea-level rise, Geophys. Res. Lett., 38, L17401, doi:
10.1029/2011GL048604.
McMahon, P. B., J. K. B€
ohlke, L. J. Kauffman, K. L. Kipp, M. K. Landon, C. A. Crandall, K. R. Burow, and C. J. Brown (2008), Source and transport
controls on the movement of nitrate to public supply wells in selected principal aquifers of the U.S., Water Resour. Res., 44, W04401, doi:
10.1029/2007WR006252.
McMahon, P. B., L. N. Plummer, J. K. B€
ohlke, S. D. Shapiro, and S. R. Hinkle (2011), A comparison of recharge rates in aquifers of the U.S.
based on groundwater-age data, Hydrogeol. J., 19(4), 779–800.
NASS (2008), Farm and Ranch Irrigation Survey, Spec. Stud. 1, vol. 3, U.S. Dep. of Agric., Wash.
NASS (2009), 2007 Census of Agriculture, Geogr. Area Ser. 51, vol. 1, U.S. Dep. of Agric., Wash.
NOAA (2014), Climate at a Glance—Time Series, Natl. Clim. Data Cent., Natl. Oceanic and Atmos. Admin.
Oki, T., and S. Kanae (2006), Global hydrological cycles and world water resources, Science, 313(5790), 1068–1072.
Pastor, A. V., F. Ludwig, H. Biemans, H. Hoff, and P. Kabat (2013), Accounting for environmental flow requirements in global water assessments, Hydrol. Earth Syst. Sci. Discuss., 10(12), 14,987–15,032.
C 2014. American Geophysical Union. All Rights Reserved.
V
4972
Water Resources Research
10.1002/2013WR014792
Poff, N. L., et al. (2009), The ecological limits of hydrologic alteration (ELOHA): A new framework for developing regional environmental
flow standards, Freshwater Biol., 55(1), 147–170.
Portmann, F. T., S. Siebert, and P. D€
oll (2010), MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new
high-resolution data set for agricultural and hydrological modeling, Global Biogeochem. Cycles, 24, GB1011, doi:10.1029/2008GB003435.
Ramankutty, N., A. T. Evan, C. Monfreda, and J. A. Foley (2008), Farming the planet. 1: Geographic distribution of global agricultural lands in
the year 2000, Global Biogeochem. Cycles, 22, GB1003, doi:10.1029/2007GB002952.
Richter, B., M. Davis, C. Apse, and C. Konrad (2012), A presumptive standard for environmental flow protection, River Res. Appl., 28(8), 1312–
1321.
Richter, B. D., R. Mathews, D. L. Harrison, and R. Wigington (2003), Ecologically sustainably water management: Managing river flows for
ecological integrity, Ecol. Appl., 13(1), 206–224.
Robinson, J. R. C., J. L. Park, and S. Fuller (2006), Cotton transportation and logistics: A dynamic system, in 2007 Annual Transportation
Research Forum. [Available at http://www.trforum.org/forum/downloads/2007_7B_CottonTrans_paper.pdf.]
Rohwer, J., D. Gerten, and W. Lucht (2007), Development of functional types of irrigation for improved global crop modelling, PIK Rep. 104,
Potsdam Inst. for Clim. Impact Res., Potsdam, Germany.
Rost, S., D. Gerten, A. Bondeau, W. Lucht, J. Rohwer, and S. Schaphoff (2008), Agricultural green and blue water consumption and its influence on the global water system, Water Resour. Res., 44, W09405, doi:10.1029/2007WR006331.
Rudolf, B., A. Becker, U. Schneider, A. Meyer-Christoffer, and M. Ziese (2010), GPCC Status Report December 2010 (on the most recent
gridded global data set issued in fall 2010 by the Global Precipitation Climatology Centre (GPCC), report, DWD/GPCC.
Scanlon, B. R., C. C. Faunt, L. Longuevergne, R. C. Reedy, W. M. Alley, V. L. McGuire, and P. B. McMahon (2012), Groundwater depletion and
sustainability of irrigation in the US High Plains and Central Valley, Proc. Natl. Acad. Sci. U. S. A., 109(24), 9320–9325.
Siebert, S., J. Burke, J. M. Faures, K. Frenken, J. Hoogeveen, P. D€
oll, and F. T. Portmann (2010), Groundwater use for irrigation—A global
inventory, Hydrol. Earth Syst. Sci., 14(10), 1863–1880.
Smakhtin, V. U., C. Revenga, and P. D€
oll (2004), A pilot global assessment of environmental water requirements and scarcity, Water Int.,
29(3), 307–317.
Solley, W. B., R. R. Pierce, and H. A. Perlman (1998), Estimated use of water in the U.S. in 1995, U.S. Geol. Surv. Circ., 1200, 71 pp.
Sumner, D. A., and H. Brunke (2003), The Economic Contributions of the California Rice Industry, Calif. Rice Comm., Sacremento, Calif.
TSHA (2012), Texas Almanac, Denton, Tex.
United States Department of Agriculture (USDA) (1997), Agricultural resources and environmental indicators, 1996–97, Agric. Handb. 712,
U.S. Dep. of Agric., Econ. Res. Serv., Nat. Resour. and Environ. Div. Wash.
van Beek, L. P. H., Y. Wada, and M. F. P. Bierkens (2011), Global monthly water stress. 1: Water balance and water availability, Water Resour.
Res., 47, W07517, doi:10.1029/2010WR009791.
Wada, Y., L. P. H. van Beek, C. M. van Kempen, J. W. T. M. Reckman, S. Vasak, and M. F. P. Bierkens (2010), Global depletion of groundwater
resources, Geophys. Res. Lett., 37, L20402, doi:10.1029/2010GL044571.
Wada, Y., L. P. H. van Beek, and M. F. P. Bierkens (2012), Nonsustainable groundwater sustaining irrigation: A global assessment, Water
Resour. Res., 48, W00L06, doi:10.1029/2011WR010562.
Weeks, J. B., E. D. Gutentag, F. J. Heimes, and R. R. Luckey (1998), Summary of the High Plains regional aquifer-system analysis in parts of
Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming, U.S. Geol. Surv. Prof. Pap., 1400-A, USGS,
30 pp.
WFPEI (2012), Comeback for cotton in California’s Tulare County, in Western Farm Press Exclusive Insight; 2/2/2012, p2, edited.
Yoshiyama, R. M., F. W. Fisher, and P. B. Moyle (1998), Historical abundance and decline of chinook salmon in the Central Valley Region of
California, N. Am. J. Fish. Manage., 18(3), 487–521.
Yoshiyama, R. M., P. B. Moyle, E. R. Gerstung, and F. W. Fisher (2000), Chinook salmon in the California Central Valley: An assessment, Fisheries, 25(2), 6–20.
ESNAULT ET AL.
C 2014. American Geophysical Union. All Rights Reserved.
V
4973